diff --git a/episodes/figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png b/episodes/figures/curatedAtlasQuery.png
similarity index 100%
rename from episodes/figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png
rename to episodes/figures/curatedAtlasQuery.png
diff --git a/episodes/hca.Rmd b/episodes/hca.Rmd
index 6be8def..65473e5 100644
--- a/episodes/hca.Rmd
+++ b/episodes/hca.Rmd
@@ -69,7 +69,7 @@ bulk counts are also available to facilitate large-scale, summary analyses of
 transcriptional profiles. This platform offers a standardized workflow for
 accessing atlas-level datasets programmatically and reproducibly.
 
-![](figures/HCA_sccomp_SUPPLEMENTARY_technical_cartoon_curatedAtlasQuery.png)
+![](figures/curatedAtlasQuery.png)
 
 # Data Sources in R / Bioconductor
 
diff --git a/episodes/intro-sce.Rmd b/episodes/intro-sce.Rmd
index baa3995..1e66c37 100644
--- a/episodes/intro-sce.Rmd
+++ b/episodes/intro-sce.Rmd
@@ -23,9 +23,9 @@ exercises: 10 # Minutes of exercises in the lesson
 # Setup
 
 ```{r setup, message = FALSE, warning=FALSE}
-library("SummarizedExperiment")
-library("SingleCellExperiment")
-library("MouseGastrulationData")
+library(SummarizedExperiment)
+library(SingleCellExperiment)
+library(MouseGastrulationData)
 library(BiocStyle)
 ```
 
diff --git a/episodes/large_data.Rmd b/episodes/large_data.Rmd
index 92b42a1..8311c02 100644
--- a/episodes/large_data.Rmd
+++ b/episodes/large_data.Rmd
@@ -17,7 +17,7 @@ exercises: 2 # Minutes of exercises in the lesson
 - Learn how to work with out-of-memory data representations such as HDF5.
 - Learn how to speed up single-cell analysis with parallel computation.
 - Learn how to invoke fast approximations for essential analysis steps.
-- Learn how to convert SingleCellExperiment objects to SeuratObjects and AnnData objects.
+- Learn how to convert `SingleCellExperiment` objects to `SeuratObject`s and `AnnData` objects.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
@@ -526,7 +526,7 @@ Use Seurat's `DimPlot` function.
 
 ::::::::::::::::::::::::::::::::::::: keypoints 
 
-- Out-of-memory representations can be used to work with single-cell datasets that are too large to fit in memory 
+- Out-of-memory representations can be used to work with single-cell datasets that are too large to fit in memory
 - Parallelization of calculations across genes or cells is an effective strategy for speeding up analysis of large single-cell datasets
 - Fast approximations for nearest neighbor search and singular value composition can speed up essential steps of single-cell analysis with minimal loss of accuracy
 - Converter functions between existing single-cell data formats enable analysis workflows that leverage complementary functionality from poplular single-cell analysis ecosystems